Google Machine Learning Platform

Almost everything we see around us has advanced technology embedded into it. One of such breakthrough technologies is artificial intelligence. Any machine that is "intelligent" can learn how to perform tasks and improve at them, an avenue explored by enterprises to their fullest. Global sending on AI and other cognitive systems will reach 57.6 million dollars in 2021 and 190 billion dollar industry by 2025. An AI platform is a space where you can train and host your machine learning models and make predictions about new data at your convenience. Google's Machine Learning Platform features a convenient location where you can do hassle-free development, with a faster production time, availability of no-code-based tools, and robust governance having interpretable models. 

Google and AI

Artificial intelligence in Google is given the highest priority. It is reflected in their mission statement, where they strive to make information more accessible universally, by organizing and structuring it. AI allows us to make sense of large datasets in real-time, a feature that is useful when the worldwide data will grow 61% by 2025. Artificial intelligence is a remarkable tool to complete this task in new and innovative ways, and thus Google promotes AI's assistance in solving problems of its users and the world. Only 37% of organizations used AI in the workplace in 2019 while only 29% of smaller businesses have adopted this technology. In numerous statements, Google promises to make Artificial Intelligence a more accessible domain, and the Google Machine Learning Platform is built to accomplish exactly that.

Google Cloud Platform 

Google Cloud forms a separate platform of its own, called the Google Cloud Platform or the GCP. It provides services ranging from hosting containerized applications, like social media apps or large-scale data analytics platforms. Apart from common use cloud-based services, it is also designed for advanced machine learning and artificial intelligence paradigms, as 75% of commercial enterprise apps will use AI by 2021. The Google Cloud is manifested as a large number of computers and hard disk drives and virtual machines across data centers of Google around the world. Its infrastructure is the same as that of Google Mail or Web. It is also a major cloud provider in the marketplace. 

Google Cloud AI Platform

The Google AI Platform is designed to cater to the needs of building, deploying, and managing models built on machine learning, and associated services in a cloud environment. Google stresses the accessibility of such services, and the machine learning platform is a hyper-accessible space for data engineers and data scientists. It can help in streamlining ML workflows to accessing groundbreaking AI models. It supports training, prediction, and version management of high-level models built using SKLearn and Tensorflow along with providing a point-and-click ML search engine, AutoML.

Google Machine Learning Platform provides a large number of services to support typical activities in an ML workflow. The five services are-

• Preparation

The preparation of data, that is, ingesting, cleaning, and feature engineering, is done through BigQuery Datasets (A collection of tables in the data warehouse of Google). BigQuery is a typical warehouse of data, and even though it is not strictly an AI service, it aids the development of AI models and is used in 99% of the ML workflows being built. For further action on data. Google provides a Data Labelling Service. Training data is labelled by human labellers and the service together to output a highly accurate label for a collection of data to be used in a machine learning model. 

• Building

To build ML models, Google provides AutoML, a platform to train models with zero code. It also provides the concept of "machines training machines". This ensures that teams having problems with training high-quality and sophisticated models have an easy-to-use graphical interface to assist them. Cloud AutoML uses Google's research technology, leveraging around 10 years of data to make machine learning models more robust and achieve fast performance and accurate predictions. Using AI Platform Notebooks, you can customize ML models. AI Platform Training also provides services on the computer and cloud side of things.

• Validation

After building the ML model, Explainable AI offers a great collection of tools to help you understand your model, ranging from its outputs to behavior, identify the biases and suggest ideas to improve both your model and your training data. In activities such as model tuning, this collection of tools saves a lot of shots in the dark. For instance, image classification models with training images containing similar backgrounds might imbibe the background information as relevant. Explainable AI can help you identify such situations. Similarly, AI Platform Vizier refines the optimization process by providing more differentiated parameters. 

• Deployment

There are a few services in Google's Machine Learning Platform that provide deployment predictions. AI Platform Prediction is used for the management of the infrastructure on which you run your model. This service makes available the infrastructure during online and batch prediction requests. AutoML Vision Edge is used for the deployment of edge models, like those run on smartphones and IoT devices. It can trigger real-time action based on local data. TensorFlow Enterprise is used in Tensorflow projects where enterprise-grade support is needed. 

• ML Pipelines (ML Ops)

To manage your model better, ML pipelines that are robust, repeatable and scalable, are deployed. Some services assist these pipelines. AI Platform Pipelines, a service that helps create ML pipelines through Kuberflow Pipelines or TensorFlow Extended (TFX). Continuous Evaluation monitors and provides continuous feedback on the performance of your models. Deep learning VM Image has provisions for integrating Cloud VMs with deep learning ML applications. Lastly, Deep Learning Containers have templates of containers for deep learning environments. These containers are preconfigured and optimized for the best use case. 

Machine Learning is the future of technological innovations and business expansions. To create an accessible environment around artificial intelligence modelling, Google's Machine Learning Platform provides numerous services that solve the problems of scalability and complexity in usage. Data scientists and data engineers can truly engage and dive deep into the realm of machine learning workflow and modelling structure. 54% of the executives have already marked an increase in productivity levels after using AI and 36% say that AI gives them the space to be more creative. The journey from ideation to deployment happens quickly and cost-effectively with Google's Machine Learning Platform.